Alternatively, we may use an information criterion to assess the quality of the estimate of the model error. In this case, the removal of a certain network edge is accepted only if the quality of the estimate does not decrease. By using an information criterion, both the value of the cost function as well as the number of network parameters are taken into account. There is a large selection of different information criteria to choose from. Several of them are described in detail in [1]} and [2]}. The choice of information criterion may significantly influence the sparsification process due to the differences in assumptions on which the various information criteria are derived. A similar approach of quality assessment has been proposed in [3]}, where Akaike's Information Criterion and the Bayesian Information Criterion are used to evaluate the results of the sparse identification approach in [4]}.
